Revelo vs ScienceSoft: full comparison for 2026
Quick verdict
Revelo (3.8/5) edges ahead of ScienceSoft (3.7/5) overall. Revelo is the better choice for hiring Latin American developers through a marketplace. ScienceSoft is the stronger option for regulated companies wanting a documented hiring process. The right choice depends on your project size, budget, and required tech stack.
Revelo vs ScienceSoft: head-to-head summary
| Criterion | Revelo | ScienceSoft |
|---|---|---|
| Founded | 2014 | 1989 |
| HQ | São Paulo, Brazil | McKinney, Texas, USA |
| Team size | 400,000+ developer network (per company) | 750+ |
| Rating | 3.8 / 5 | 3.7 / 5 |
| Primary differentiator | A very large Latin American pool with payroll and compliance included | Publishes its staff augmentation timeline and process |
| Pricing model | Marketplace placement with monthly billing; rates on request | Hourly or monthly rates shared with CVs; time and materials |
| Min. engagement | Not published | Not published |
| Primary tech stack | Python, OpenAI, Hugging Face | Python, Azure ML, AWS |
| Industries served | Software & SaaS, AI research labs, Financial services | Healthcare & life sciences, Financial services, Manufacturing, Retail & e-commerce |
Revelo vs ScienceSoft: overview
Revelo
Revelo was founded in late 2014 in Brazil (some sources say 2015) and began as a domestic hiring platform called Contratado. It now runs a network of more than 400,000 Latin American developers and handles hiring and payment for U.S. customers. TechCrunch reported that work on foundation models made up 22% of Revelo's revenue in 2024. Revelo is a marketplace, so engineers are matched through its platform rather than employed in a delivery center.
ScienceSoft
ScienceSoft dates its IT work to 1989 and is headquartered in McKinney, Texas. It says its staff augmentation pool covers more than 750 professionals, including data scientists with long industry experience, and it publishes a fast hiring sequence: CVs with rates within a day, interviews in two to four days and starts in one to two weeks (per company website; independently unverifiable). AI is one of many service areas alongside its long-standing healthcare and finance work.
Services and capabilities: Revelo vs ScienceSoft
| Capability | Revelo | ScienceSoft |
|---|---|---|
| LLM / GenAI engineers | ✓ | ✗ |
| MLOps & deployment | ✗ | ✗ |
| Computer vision | ✗ | ✗ |
| Data engineering | ✗ | ✓ |
| AI agent development | ✗ | ✗ |
| Fractional / part-time experts | ✗ | ✗ |
| Risk-free trial period | ✗ | ✗ |
| Nearshore time-zone overlap | ✓ | ✗ |
Tech stack comparison: Revelo vs ScienceSoft
| Framework / platform | Revelo | ScienceSoft |
|---|---|---|
| PyTorch | N/A | N/A |
| TensorFlow | N/A | N/A |
| LangChain | N/A | N/A |
| Hugging Face | ✓ | N/A |
| OpenAI | ✓ | N/A |
| AWS | ✓ | ✓ |
| Azure | N/A | ✓ |
| Databricks | N/A | N/A |
| MLflow | N/A | N/A |
| Kubernetes | N/A | N/A |
Pricing comparison: Revelo vs ScienceSoft
| Criterion | Revelo | ScienceSoft |
|---|---|---|
| Minimum engagement | Not published | Not published |
| Engagement models | Full-time dedicated engineers | Full-time dedicated engineers, Dedicated team |
| Rate transparency | Not public | Not public |
| Price tier | Mid-market | Mid-market |
Target audience comparison: Revelo vs ScienceSoft
| Dimension | Revelo | ScienceSoft |
|---|---|---|
| Best company size | Startup to mid-market | Startup to mid-market |
| Best industries | Software & SaaS, AI research labs, Financial services | Healthcare & life sciences, Financial services, Manufacturing |
| Best use cases | Hiring LLM data specialists for a model-training effort, Adding a Brazilian developer to a U.S. SaaS team | Adding a data scientist to a healthcare analytics team, Staffing BI and ML roles for a manufacturer |
| Typical project type | Full-time dedicated engineers | Full-time dedicated engineers |
Revelo vs ScienceSoft: pros and cons
| Revelo | |
|---|---|
| + | Very large pool across Latin America |
| + | Handles hiring, payroll and compliance |
| + | Foundation-model work gives some engineers LLM training experience |
| - | Marketplace matching means quality varies by candidate |
| - | Founding year is reported as both 2014 and 2015 |
| - | Less hands-on management than employer-based firms |
| ScienceSoft | |
|---|---|
| + | Shares rates together with candidate CVs |
| + | Long history in healthcare and finance |
| + | Clear published hiring timeline |
| - | AI is a small part of a very wide catalog |
| - | Fewer GenAI specialists than AI-focused firms |
| - | Speed figures come from its own marketing |
Who should choose Revelo?
A typical fit: hiring LLM data specialists for a model-training effort.
A very large Latin American pool with payroll and compliance included. Minimum engagement is not publicly disclosed. Works best with clients in Software & SaaS, AI research labs, Financial services.
Who should choose ScienceSoft?
A typical fit: adding a data scientist to a healthcare analytics team.
Publishes its staff augmentation timeline and process. Minimum engagement is not publicly disclosed. Works best with clients in Healthcare & life sciences, Financial services, Manufacturing, Retail & e-commerce.
Decision matrix: Revelo vs ScienceSoft
| Your situation | Recommended choice |
|---|---|
| You need a dedicated team for a long programme | ScienceSoft |
| You want the supplier to own delivery as well as staffing | Neither offers managed delivery; you will lead the work |
| You need one expert part-time | Neither lists part-time experts; ask about reduced hours |
| You want to test an engineer before signing for months | Neither publishes a trial; ask for a short first term |
| Your budget is at the lower end | Compare: Revelo (Not published) vs ScienceSoft (Not published) |
| You need overlap with U.S. working hours | Revelo |
| You need specialist depth in a specific vertical | ScienceSoft |
Use case fit: Revelo vs ScienceSoft
| Use case | Revelo fit | ScienceSoft fit | Winner |
|---|---|---|---|
| Hiring LLM data specialists for a model-training effort | Strong | Limited | Revelo |
| Adding a Brazilian developer to a U.S. SaaS team | Strong | Strong | Both equally |
| Adding a data scientist to a healthcare analytics team | Strong | Strong | Both equally |
| Staffing BI and ML roles for a manufacturer | Limited | Strong | ScienceSoft |
Verdict: Revelo vs ScienceSoft
Revelo (3.8/5) is the stronger overall choice for most AI Staff Augmentation projects. A very large Latin American pool with payroll and compliance included.
ScienceSoft (3.7/5) is worth a look if you need staffing BI and ML roles for a manufacturer. If your situation matches that, ScienceSoft is a competitive option.
Related comparisons
Revelo vs ScienceSoft FAQ
Is Revelo better than ScienceSoft?
Revelo (3.8/5) scores higher overall, but "better" depends on your use case. Revelo's strongest advantage: very large pool across Latin America. ScienceSoft's strongest advantage: shares rates together with candidate CVs.
How do Revelo and ScienceSoft differ in pricing?
Revelo uses marketplace placement with monthly billing; rates on request pricing. ScienceSoft uses hourly or monthly rates shared with cvs; time and materials pricing. Neither firm publishes a full rate card; a discovery call is required for project-specific quotes.
Which is better for enterprise: Revelo or ScienceSoft?
Revelo is the larger team and typically the better enterprise-scale choice. For very large programmes, verify team size and compliance coverage directly with each company before shortlisting.
What are the main differences between Revelo and ScienceSoft?
Revelo's primary differentiator is: a very large Latin American pool with payroll and compliance included. ScienceSoft's primary differentiator is: publishes its staff augmentation timeline and process. They also differ in team size (400,000+ developer network (per company) vs 750+), minimum engagement (Not published vs Not published), and primary industries served (Software & SaaS, AI research labs vs Healthcare & life sciences, Financial services).
Verify all details directly with each company before making a decision.